PUBLIC AND PRIVATE UTILISATION OF IN-PATIENT BEDS IN IRISH ACUTE PUBLIC HOSPITALS. ESRI Research Bulletin 2010/4/5

Health care systems in many developed countries have services financed and provided by both public and private sectors. In Ireland, though, the public/private mix is atypical: a private patient can be treated in an acute public hospital and seen by a consultant who may also treat public patients within the same hospital. Nationally, one in five beds in acute public hospitals is designated for use by private patients and existing legislation restricts accommodation of a private patient in a public-designated bed. Yet there are concerns that acute public hospitals may sidestep such restrictions on their private practice, resulting in public hospital resources potentially being diverted away from public patients towards their private counterparts. Indeed, Irish providers face financial incentives which favour the treatment of private patients. Consultants are rewarded on a fee-for-service basis for private care, but receive a salary for public practice. Public hospitals, meanwhile, receive a fixed daily payment for every private patient in a private bed. Added to these financial incentives is an increased opportunity to engage in private practice due to the substantial recent growth in private health insurance subscribers

RESOURCE ALLOCATION, FINANCING AND SUSTAINABILITY IN THE HEALTH SECTOR. ESRI Research Bulletin 2010/3/1

The focus on acute, episodic care in the conventional health-care model fails to provide adequately for changing health-care needs arising from increased longevity and increasing prevalence of chronic disease. Integrated care involves coherent and co-ordinated delivery of health-care services across a broad range of health and social care providers. A principal aim of integrated health care is to improve the patient’s journey through the system by co-ordinating care among providers and by strengthening the role of primary care. Effective resource allocation mechanisms, supported by appropriate financing arrangements, have an important role to play in delivering integrated health care. In addition, more efficient use of scarce health-care resources is required, and can be influenced by the resource allocation and financing mechanisms. This article summarises research undertaken by the ESRI to provide evidence for the Expert Group on Resource Allocation and Financing in Health Care, which reported in July 2010 (Brick et al., 2010a, b; Ruane, 2010). The research: • reviewed the theoretical and international empirical literature on resource allocation, financing and sustainability in health care (focusing on eight comparator countries – Australia, Canada, Germany, Netherlands, New Zealand, Sweden, UK, USA); • evaluated current Irish systems of resource allocation and financing and issues associated with sustainability; • proposed a framework for health-care entitlements and user fees that would support the delivery of integrated health care in Ireland

Explaining consequences of employment insecurity: The dynamics of scarring in the United Kingdom, Poland and Norway

This deliverable presents three country studies on scarring effects of early employment insecurity in the United Kingdom, Poland and Norway. Traditional analysis of scarring effects has favoured the analysis of the impact of the experience of unemployment on the experience of subsequent unemployment (state dependence) and the monetary costs of previous unemployment in terms of lower subsequent wages (see e.g. Arulampalam, Booth and Taylor 2000; Arulampalam, Gregg and Gregory 2001). The three present country studies go beyond the traditional analysis of scarring effects in order to better understand the trade-offs experienced by young female and male workers when faced with an insecure labour market integration. With national longitudinal data, original methodological designs and research focus, each study contributes in an original way to the research literature. All three studies pay special attention to gender and education as potential moderating variables of scarring effects

A means of assessing deep learning-based detection of ICOS protein expression in colon cancer.

Biomarkers identify patient response to therapy. The potential immune‐checkpoint bi-omarker, Inducible T‐cell COStimulator (ICOS), expressed on regulating T‐cell activation and involved in adaptive immune responses, is of great interest. We have previously shown that open-source software for digital pathology image analysis can be used to detect and quantify ICOS using cell detection algorithms based on traditional image processing techniques. Currently, artificial intelligence (AI) based on deep learning methods is significantly impacting the domain of digital pa-thology, including the quantification of biomarkers. In this study, we propose a general AI‐based workflow for applying deep learning to the problem of cell segmentation/detection in IHC slides as a basis for quantifying nuclear staining biomarkers, such as ICOS. It consists of two main parts: a simplified but robust annotation process, and cell segmentation/detection models. This results in an optimised annotation process with a new user‐friendly tool that can interact with1 other open‐source software and assists pathologists and scientists in creating and exporting data for deep learning. We present a set of architectures for cell‐based segmentation/detection to quantify and analyse the trade‐offs between them, proving to be more accurate and less time consuming than traditional methods. This approach can identify the best tool to deliver the prognostic significance of ICOS protein expression

ICOSeg: real-time ICOS protein expression segmentation from immunohistochemistry slides using a lightweight conv-transformer network.

In this article, we propose ICOSeg, a lightweight deep learning model that accurately segments the immune-checkpoint biomarker, Inducible T-cell COStimulator (ICOS) protein in colon cancer from immunohistochemistry (IHC) slide patches. The proposed model relies on the MobileViT network that includes two main components: convolutional neural network (CNN) layers for extracting spatial features; and a transformer block for capturing a global feature representation from IHC patch images. The ICOSeg uses an encoder and decoder sub-network. The encoder extracts the positive cell's salient features (i.e., shape, texture, intensity, and margin), and the decoder reconstructs important features into segmentation maps. To improve the model generalization capabilities, we adopted a channel attention mechanism that added to the bottleneck of the encoder layer. This approach highlighted the most relevant cell structures by discriminating between the targeted cell and background tissues. We performed extensive experiments on our in-house dataset. The experimental results confirm that the proposed model achieves more significant results against state-of-the-art methods, together with an 8× reduction in parameters

Household employment and the crisis in Europe

The 2008 crisis had a significant impact on household employment in some European countries. An analysis of the EU Statistics on Income and Living Conditions generated a new cross-national typology of household employment structures and showed how these changed during the crisis and austerity period, capturing the experiences of high and low qualified households. Findings indicate that dual earning households are not always a consequence of gender equality but result from economic necessity or employment opportunities. The re-emergence of traditional male breadwinner households is often the result of female unemployment, especially for lower educated women. An increase in female single earners and workless households is evident in countries hit hardest by the employment crisis. The value of this cross-national typology, rooted in the interaction of educational effects and employment opportunities, is allowing comparison both within and between European countries, going beyond established typologies based on policy frameworks or gender cultures

Why the trial researcher matters: Day-to-day work viewed through the lens of normalization process theory

Researchers working in the field, the places where research-relevant activity happens, are essential to recruitment and data collection in randomised controlled trials (RCTs). This study aimed to understand the nature of this often invisible work. Data were generated through an RCT of a pharmacist-led medication management service for older people in care homes. The study was conducted over three years and employed seven Research Associates (RA) working in Scotland, Northern Ireland, and England. Weekly research team meetings and Programme Management Group meetings naturally generated 129 sets of minutes. This documentary data was supplemented with two end-of-study RA debriefing meetings. Data were coded to sort the work being done in the field, then deductively explored through the lens of Normalization Process Theory to enable a greater understanding of the depth, breadth and complexity of work carried out by these trial delivery RAs. Results indicate RAs helped stakeholders and participants make sense of the research, they built relationships with participants to support retention, operationalised complex data collection procedures and reflected on their own work contexts to reach agreement on changes to trial procedures. The debrief discussions enabled RAs to explore and reflect on experiences from the field which had affected their day-to-day work. The learning from the challenges faced in facilitating care home research may be useful to inform future research team preparation for complex interventions. Scrutinising these data sources through the lens of NPT enabled us to identify RAs as linchpins in the successful conduct of a complex RCT study